Learning classification in the olfactory system of insects
Learning classification in the olfactory system of insects
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DOI：
10.1162/089976604774201613
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发表时间：
2004-08-01
影响因子：
2.9
通讯作者：
Rabinovich, MI
中科院分区：
文献类型：
--
作者：
Huerta, R;Nowotny, T;Rabinovich, MI
我们提出了一个理论框架的气味分类在嗅觉系统的昆虫。分类任务分两步完成。第一个是从触角叶到蘑菇体内的固有凯尼恩细胞的转变。这种向更高维空间的转换是一个单射函数，可以在突触连接处不需要任何类型的学习的情况下实现。在第二步中，编码的气味在内在凯尼恩细胞线性分类的蘑菇体叶。执行这种线性分类的神经元相当于超平面，其连接通过局部赫布学习和相互抑制引起的竞争来调整。我们计算的范围内的活动和规模的网络所需的值，以实现有效的分类在该计划中的昆虫嗅觉。我们能够证明，生物合理的控制机制可以实现有效的分类气味。
We propose a theoretical framework for odor classification in the olfactory system of insects. The classification task is accomplished in two steps. The first is a transformation from the antennal lobe to the intrinsic Kenyon cells in the mushroom body. This transformation into a higher-dimensional space is an injective function and can be implemented without any type of learning at the synaptic connections. In the second step, the encoded odors in the intrinsic Kenyon cells are linearly classified in the mushroom body lobes. The neurons that perform this linear classification are equivalent to hyperplanes whose connections are tuned by local Hebbian learning and by competition due to mutual inhibition. We calculate the range of values of activity and size of the network required to achieve efficient classification within this scheme in insect olfaction. We are able to demonstrate that biologically plausible control mechanisms can accomplish efficient classification of odors.